generateTimeSeries {BoolNet} | R Documentation |

## Generate time series from a network

### Description

Generates time series by simulating successive state transitions from random start states. In addition, the resulting matrices can be perturbed by Gaussian noise.

### Usage

```
generateTimeSeries(network,
numSeries,
numMeasurements,
type = c("synchronous","asynchronous","probabilistic"),
geneProbabilities,
perturbations = 0,
noiseLevel = 0)
```

### Arguments

`network` |
An object of class |

`numSeries` |
The number of random start states used to generate successive series of states, that is, the number of time series matrices to generate |

`numMeasurements` |
The number of states in each of the time series matrices. The first state of each time series is the randomly generated start state. The remaining |

`type` |
The type of state transitions to be performed (see |

`geneProbabilities` |
An optional vector of probabilities for the genes if |

`perturbations` |
If this argument has a value greater than 0, artificial perturbation experiments are generated. That is, |

`noiseLevel` |
If this is non-zero, it specifies the standard deviation of the Gaussian noise which is added to all entries of the time series matrices. By default, no noise is added to the time series. |

### Value

A list of matrices, each corresponding to one time series. Each row of these matrices contains measurements for one gene on a time line, i. e. column `i+1`

contains the successor states of column `i+1`

. If `noiseLevel`

is non-zero, the matrices contain real values, otherwise they contain only 0 and 1.

If `perturbations>0`

, the result list contains an additional matrix `perturbations`

specifying the artificial perturbations applied to the different time series. This matrix has `numSeries`

columns and one row for each gene in the network. A matrix entry is 0 for a knock-out of the corresponding gene in the corresponding time series, 1 for overexpression, and NA for no perturbation.

The result format is compatible with the input parameters of `binarizeTimeSeries`

and `reconstructNetwork`

.

### See Also

`stateTransition`

, `binarizeTimeSeries`

, `reconstructNetwork`

### Examples

```
## Not run:
# generate noisy time series from the cell cycle network
data(cellcycle)
ts <- generateTimeSeries(cellcycle, numSeries=50, numMeasurements=10, noiseLevel=0.1)
# binarize the noisy time series
bin <- binarizeTimeSeries(ts, method="kmeans")$binarizedMeasurements
# reconstruct the network
print(reconstructNetwork(bin, method="bestfit"))
## End(Not run)
```

*BoolNet*version 2.1.9 Index]